Data Annotation, Crucial Success Factor for AI/ML

Posted on : November 30th 2022

AI (Artificial Intelligence) and ML (Machine Learning) are the fastest-growing and most popular technologies supporting next-gen innovations. According to Forrester, “AI’s share of software spend will increase from 4.3% in 2021 to 6% in 2025” . However, for the success of these initiatives, data annotation or labeling is indispensable. High-quality training data is needed to train your artificial intelligence for machine learning processes, and data annotation seamlessly makes that happen for you.

Now let us see what data annotation is

38% of the technical leaders in a survey cited data annotation as the technology they will have by the end of 2022. They have also indicated that textual data is likely to be predominantly used in AI applications.

For enterprises, analysis of textual data retrieved, for example, from customer opinion regarding a product, can be used in market analysis and prediction. This is where data annotation solutions become indispensable. These solutions can mark, label, tag, transcribe, or process to make textual and other types of data machine-readable.

Furthermore, data annotation is a human-led process that identifies. It labels respective data types to make it more manageable for machines to classify and organize information as humans do – and to make predictions. The annotated data helps ML models identify and process the data as per the training data sets and gives appropriate results per the organizational requirement, which is essentially the way ML algorithms work.

But what are ML algorithms?

Algorithms in programming and processes are a type of command/rule that tells the machine what to do. An ML algorithm is a technique that leverages advanced technologies, such as natural language processing, deep learning, etc., to enable an AI system to perform its task. Let’s say, for example, a sorting algorithm helps a machine to sort the data in some form as per the requirement. Nevertheless, if this data is not labeled well for the sorting machine to understand, then the process is in vain.

How significant is the data annotation’s contribution?

Since we’ve established that if you would like your AI models to deliver optimum results, you need to power it with the data annotation workhorse. This workhorse enables ML algorithms to better understand the raw/input data and function.

Here is how data annotation:

Enhances the precision of AI and ML : Data that is accurately annotated or labeled helps the machine understand and process it better and, in turn, gives precise and accurate output compared to poorly labeled data.

Fast tracks AI/ML model training : With the help of data annotation, a machine understands what it needs to do with the input data that is fed to it. Studies show that the TAT is reduced to as much as 54% for data analytics to analyze the given data with efficient data annotation.

Promotes effortless composition of labeled datasets : Data annotation streamlines the processes by feeding the machine accurately labeled datasets, eventually training the machine faster and more effectively to produce speedier output.

Provides flawless user experience : Well-annotated data for an ML-powered AI system helps it to identify and address the user query/doubt and provide appropriate aid to attain satisfactory results. The understanding of the relevance of raw data happens through data annotation, which then processes it appropriately to give the user an accurate output.

Assists in AI process enhancement : The hypothesis that increasing data volume grows the AI models' precision can only prevail if a perfect data annotation process exists to feed the models with accurately annotated/labeled data. Hence, data annotation ensures that soaring data volumes do enhance the AI engine's reliability.

Enables scalability and expansion of usage : Data annotation can collate and label various genres of data, such as sentiments, intents, actions, etc., from multiple requests facilitating the generation of appropriate and versatile training datasets. Hence, the data professionals like data scientists, AI engineers, and more are empowered to scale their AI/ML projects with diverse datasets with any volume.

How is the forecast in light of technological advancements?

AI is defining the future of nearly every industry. 78% of IT and business leaders said their organization is considering or has already deployed machine-learning technologies as part of their digital business strategy. It is the driving force behind making digital enterprises a reality. AI is already enabling machines and processes to gather insights from the significant volumes of data generated by these digital enterprises. Data annotation and labeling solutions fulfill the critical and specific role of providing enterprises with quality training data for their AI models.

Besides, there is increasing adoption of annotated data to train AI models and ML systems for data extraction from complex documents. Moreover, the demand to automate analytics has led to the constantly increasing use of ML, thus elevating the need for data annotation solutions. With enterprises increasingly using computer vision, the need for end-to-end automated data annotation solutions will be X-factor in data-driven decision-making.

Furthermore, the use cases of data annotation solutions are becoming broad and varied daily. It is no wonder that according to a report, the demand for these solutions is expected to reach USD 5,331 million by 2030.

How can Straive help you?

Enterprises can quickly scale up to the demand for data annotations by outsourcing it to their trusted partners. We at Straive can take up these as projects and, by bringing in our tools, platform, and required human tasks, can act as an outsourcing partner to fulfill these requirements. It frees up your data science team to focus on developing robust models and algorithms while we take care of the data annotations. With our deep roots in content and data business and the data annotation tools and expertise, we offer the following:

Top-notch experts : Our data annotation team of skilled, experienced professionals is trained to seamlessly adapt to new training data requirements and annotate data for ML modeling purposes. Moreover, our project management team ensures that we have complete control over the quality, cost, and schedule and provide visibility to our customers.

Quality Assured : As a first step, Straive employs only annotators who can deliver 99 percent quality for all our projects. Over the years, Straive has built a set of best practices that ensure that the annotation requirements of a project are understood correctly. Subsequently, the annotations are evaluated against task requirements, a golden set, or collaboratively within the team by consensus.

Flexibility to scale up or down : A seasoned data annotation service provider like Straive leverages a tried and tested combination of skilled annotators and customizable data annotation tools to deliver high-quality annotated data efficiently, quickly, and at an industrial scale.

No entry to Internal Bias : It is essential to eliminate unintentionally introduced biases. Generally, awareness can help prevent bias. Our experienced team resolves data bias by deciding where it occurs and removing it. For this, the project management team ensures diversity by bringing in data from multiple sources and establishing expectations so that our annotators know the steps to follow in case of biased data.

Straive is a leading data annotation service provider with a skilled and experienced team. We offer technically advanced data labeling and annotation solution through a robust platform built on the latest technologies hosted on the cloud and with abilities to deploy a client-specific platform in days. Our data annotation solution can reduce expenses and hassles by managing the annotation workforce.